Chris K. Anderson

Improving hospitality industry sales: twenty-five years of revenue management.

From its origin in the airline industry nearly sixty years ago, revenue management has expanded to other hospitality industries, notably lodging and rental cars. More recently, “nontraditional” service industries, such as restaurants, golf courses, and casinos, have begun to adapt and apply revenue management principles. The first revenue-management—related article appeared in the Cornell Quarterly in 1988—an article that introduced revenue management concepts to the hotel industry. Subsequently, the Quarterly has published numerous revenue management studies, covering hotels, restaurants, golf courses, and function space. In addition, CQ has examined distribution and pricing issues. This review of twenty-plus years of CQ articles documents the expansion of revenue management themes as published in the Quarterly and suggests directions for future revenue management research.

Setting Prices on Priceline

Priceline is best known for its name-your-own-price format, in which consumers bid for services but not for service providers. Because Priceline serves as an opaque selling mechanism, it attracts price-conscious consumers. Sellers also benefit because they can price into multiple market segments without worrying that they are diluting revenue they might receive from customers who are willing to use conventional selling channels and pay more. A firm that releases its inventory to Priceline must manage the trade-off of pricing its inventory too low and forgoing revenue versus pricing it too high and forgoing a sale. In this paper, we outline the mechanism that Priceline uses to determine if customer bids are successful and, given this mechanism, establishes optimal prices and inventory allocations for Kimpton Hotels.

Revenue management for low-cost providers

Low-cost providers have emerged as important players in many service industries, the most predominant being low-cost, or the so-called discount airlines. This paper presents models and results leading toward understanding the revenue management outlook for a discount pricing firm. A framework and model is formulated specifically for the airline industry, but is generalizable to low-cost providers in similar revenue management settings. We formulate an optimal pricing control model for a firm that must underprice to capture a segment of exogenous demand. Two specific model formulations are considered: a continuous deterministic version, and a discrete stochastic version. Structural results are derived for the deterministic case, providing insight into the general form of optimal underpricing policies. The stochastic results support the structural insight from the deterministic solution, and illuminate the effect of randomness on the underpricing policies.

Pricing and market segmentation using opaque selling mechanisms

In opaque selling certain characteristics of the product or service are hidden from the consumer until after purchase, transforming a differentiated good into somewhat of a commodity. Opaque selling has become popular in service pricing as it allows firms to sell their differentiated products at higher prices to regular brand loyal customers while simultaneously selling to non-loyal customers at discounted prices. We develop a stylized model of consumer choice that illustrates the role of opaque selling in market segmentation. We model a firm selling a product via three selling channels: a regular full information channel, an opaque posted price channel and an opaque bidding channel where consumers specify the price they are willing to pay. We illustrate the segmentation created by opaque selling as well as compare optimal revenues and prices for sellers using regular full information channels with those using opaque selling mechanisms in conjunction with regular channels. We also study the segmentation and policy changes induced by capacity constraints.

Online Low-Price Guarantees---A Real Options Analysis

A common practice among large retailers is the low-price guarantee, rebating consumers if they find an identical product cheaper elsewhere. This provides consumers with some level of comfort in their purchase decision. A similar low-price guarantee is provided by numerous service industries that allow reservation of capacity, yet do not penalize the consumer for failure to keep that reservation---examples include hotels and car rental. Given that a consumer is not required to keep the reservation, they may make another reservation, either at a competing firm or the same firm, if future prices decline. The increasing availability of pricing information on the Internet affords consumers the opportunity to be more strategic in their purchasing behavior. As consumers, we are able to quickly and easily check prices from numerous service or goods providers. The ease of price information potentially makes these guarantees very costly to the service or good provider. We analyze the implied costs associated with these guarantees by making analogies to financial options. Motivation for this research comes from a large car rental firm, Dollar Thrifty Automotive Group Inc., that considered offering a low-price guarantee to all consumers that book a reservation though their website.

An evaluation of strategies to reduce waiting times for total joint replacement in Ontario.

Background:In 2005, the median waiting time for total hip and knee joint replacements in Ontario was greater than 6 months, which is considered longer than clinically appropriate. Demand is expected to increase and exacerbate already long waiting times. Solutions are needed to reduce waiting times and improve waiting list management. Methods:We developed a discrete event simulation model of the Ontario total joint replacement system to evaluate the effects of 4 management strategies on waiting times: (1) reductions in surgical demand; (2) formal clinical prioritization; (3) waiting time guarantees; and (4) common waiting list management. Results:If the number of surgeries performed increases by less than 10% each year, then demand must be reduced by at least 15% to ensure that, within 10 years, 90% of patients receive surgery within their maximum recommended waiting time. Clinically prioritizing patients reduced waiting times for high-priority patients and increased the number of patients at all priority levels who received surgery each year within recommended maximum waiting times by 9.3%. A waiting time guarantee for all patients provided fewer surgeries within recommended waiting times. Common waiting list management improved efficiency and increased equity in waiting across regions. Discussion:Dramatically increasing the supply of joint replacement surgeries or diverting demand for surgeries to other jurisdictions will reduce waiting times for total joint replacement surgery. Introducing a strictly adhered to patient prioritization scheme will ensure that more patients receive surgery within severity-specific waiting time targets. Implementing a waiting time guarantee for all patients will not reduce waiting times—it will only shuffle waiting times from some patients to others. To reduce waiting times to clinically acceptable levels within 10 years, increases in the number of surgeries provided greater than those observed historically or reductions in demand are needed.

Strategic Operations Research and the Edelman Prize Finalist Applications 1989--1998

In an earlier study we examined the available evidence on the Edelman Prize finalist applications 1989--1998. This study concluded that 13 of the 42 private sector applications provide examples of strategic operations research (SOR) when SOR is defined as operations research that creates a sustainable competitive advantage. In a follow-up study we tested our classifications, gathering longitudinal information on the continued success of the Edelman applications. We contacted people who were familiar with all the private sector applications that were Edelman finalists 1989--1996 and had at least five years of history since the competition. We describe the post-Edelman history of these applications and use this data to reassess their strategic role. We found that the longitudinal data provides evidence to support our original classification, but also suggests that several additional applications were more strategic than was originally apparent. We conclude that almost 60% (20 of 34) of these applications created a sustainable competitive advantage for their firms and provide examples of SOR.

Employee Stock Option Valuation with an Early Exercise Boundary

Many companies are recognizing that the Black–Scholes formula is inappropriate for employee stock options (ESOs) and are moving toward lattice models for accounting or decision-making purposes. In the most influential of these models, the assumption is that employees exercise voluntarily when the stock price reaches a fixed multiple of the strike price, effectively introducing a “horizontal” exercise boundary into the lattice. In practice, however, employees make a trade-off between intrinsic value captured and the opportunity cost of time value forgone. The model proposed here explicitly recognizes and accounts for this reality and is intuitively appealing, easily implemented, and compliant with U.S. accounting standards. Employee stock options (ESOs) must be valued for accounting and economic purposes, but increasing numbers of companies are recognizing that variants of the Black–Scholes formula are inappropriate for this purpose. They are moving toward the use of lattice models—for example, the binomial option-pricing model. The most influential lattice model for option pricing assumes that employees exercise voluntarily when the stock price reaches a fixed multiple, M, of the strike price. Conceptually, this approach introduces a “horizontal” voluntary exercise boundary into the lattice. Empirical evidence suggests, however, that employees make a trade-off between intrinsic value captured and the opportunity cost of time value forgone. So, the stock must be at a relatively high multiple of the strike price to induce voluntary exercise early in the ESO life, whereas later on, employees are willing to exercise at relatively low multiples of the strike price. We propose a model that explicitly recognizes and accounts for this reality. We assume that employees exercise voluntarily when the “moneyness” of the option reaches a fixed proportion, which we term μ, of its remaining Black–Scholes value. This approach results in an intuitively appealing downward-sloping voluntary exercise boundary. Our μ model is compliant with Statement of Financial Accounting Standards No. 123 (revised), Share-Based Payment, is easily implemented, and readily encompasses such ESO characteristics as vesting restrictions, forfeiture, and forced early exercise as a result of employment termination. We show why our model may be less prone to bias than both the M model and the modified Black–Scholes model when parameter inputs are calculated from historical observations of voluntary exercise behavior. Given the known early exercise trade-off made by employees, a company that has enjoyed rapid stock price growth will probably have experienced ESO exercises at somewhat high multiples of strike price (and early in the ESO lives), so using these historical observations to calibrate an M model leads to high ESO valuations (but using the modified Black–Scholes model leads to low values). Conversely, a company that has experienced sluggish stock price growth will have experienced ESO exercises at comparatively low multiples of strike price (and later in the ESO lives), so an M model will produce low ESO values (and a Black–Scholes model will produce high valuations). To the extent that our exercise boundary better describes the exercise decisions of employees than do other models, our model is less susceptible to the biases caused by atypical stock price histories. We illustrate this comparison analytically by simulating stock price paths with a well-known utility-based model of employee exercise as a benchmark. Our results have implications for compensation committees and consultants who need to understand the potential economic cost of ESO awards to executives and employees. The results are also relevant to practitioners who are selecting ESO valuation models for accounting disclosure purposes. Academic researchers and other users of financial statements will find our results important for understanding the sensitivity of the disclosed data to the choice of valuation model and to the estimation of parameter values—which are themselves dependent on the chosen historical dataset. Editor’s Note: The paper on which this article is based won the Second Annual “Best Conference Research Paper Award” from the Canadian Finance Executives Research Foundation at the 2008 Financial Executives International (FEI) Canada conference.

Room-Risk Management at Sunquest Vacations

This article outlines some of the basic complexities that originate with the acquisition of hotel rooms for a reseller of bundled vacations. A tour operator typically acquires or contracts for service capacity, bundles the services (air, hotel, food and beverage, and excursions), then markets and sells to consumers. The article focuses on the short-term aspects of room-risk management for the tour operator, specifically how to manage blocks of take-or-pay contracted rooms. The room-risk management problem is formulated as a math program with the objective of minimizing wasted rooms. While the exposition focuses on a particular reseller of packaged vacations, the method is applicable to any firm acquiring capacity on take-or-pay contracts and reselling this capacity as bundled vacations.

Pricing competition and channel coordination in the tourism supply chain with optional tours

The authors consider a tourism supply chain that consists of a tour operator in the source market and a local operator at the destination. The tour product is composed of predesigned tours and optional tours. Consumers are sensitive to the price and the availability of optional tours, represented as the ratio of optional tours. The authors analyse how the ratio of optional tours to predesigned tours affects each players equilibrium decisions given three different consumer attitudes towards optional tours. They find that when the channel is coordinated and the ratio of optional tours is sufficiently large, the local operator may reduce commissions. To curb the impacts of lowering commissions, the authors introduce a tax mechanism aimed at optional tours. Numeral examples are provided to illustrate the pricing impacts of optional tours.